Search Results for "metasploitable-2" - Page 8
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Showing 2997 open source projects for "metasploitable-2"
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JAX
Composable transformations of Python+NumPy programs
...It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order. What’s new is that JAX uses XLA to compile and run your NumPy programs on GPUs and TPUs. Compilation happens under the hood by default, with library calls getting just-in-time compiled and executed. But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. ... -
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Autograd
Efficiently computes derivatives of numpy code
...It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization. For more information, check out the tutorial and the examples directory. We can continue to differentiate as many times as we like, and use numpy's vectorization of scalar-valued functions across many different input values. -
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Haiku Sonnet for JAX
JAX-based neural network library
...Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX's pure function transformations. Haiku provides two core tools: a module abstraction, hk.Module, and a simple function transformation, hk.transform. hk.Modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. hk.transform turns functions that use these object-oriented, functionally "impure" modules into pure functions that can be used with jax.jit, jax.grad, jax.pmap, etc. -
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DoWhy
DoWhy is a Python library for causal inference
...DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. -
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atpbar
Progress bars for threading and multiprocessing tasks on terminal
Progress bars for threading and multiprocessing tasks on the terminal and Jupyter Notebook. atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on the terminal and Jupyter Notebook. atpbar can be used with Mantichora. atpbar started its development in 2015 as part of Alphatwirl. atpbar prevented physicists from terminating their running analysis codes, which... -
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nginx-proxy
Automated nginx proxy for Docker containers using docker-gen
nginx-proxy sets up a container running nginx and docker-gen. docker-gen generates reverse proxy configs for nginx and reloads nginx when containers are started and stopped. The containers being proxied must expose the port to be proxied, either by using the EXPOSE directive in their Dockerfile or by using the --expose flag to docker run or docker create and be in the same network. By default, if you don't pass the --net flag when your nginx-proxy container is created, it will only be... -
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GitGutter
A Sublime Text 2/3 plugin to see git diff in gutter
A Sublime Text plug-in to show information about files in a git repository. Gutter Icons indicating inserted, modified or deleted lines. Diff Popup with details about modified lines. Status Bar Text with information about file and repository and provides some commands like Goto Change to navigate between modified lines. Copy from Commit to copy the original content from the commit. Revert to Commit to revert a modified hunk to the original state in a commit. The diff popup shows the original... -
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RecAI
Bridging LLM and Recommender System
...Traditional recommender systems rely on structured behavioral data such as user interactions and item embeddings, while large language models excel at understanding language and reasoning about user preferences. RecAI aims to bridge these two domains by creating architectures and training methods that allow LLMs to function as intelligent recommendation engines. The project explores several approaches, including fine-tuning language models using user behavior data, building recommender agents, and using LLMs to explain recommendation results. RecAI also investigates how conversational interfaces powered by LLMs can improve the personalization and transparency of recommendation systems. -
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Django RQ
A simple app that provides django integration for RQ
...You can provide your own singleton Redis connection object to this function so that it will not create a new connection object for each queue definition. If you have django-redis or django-redis-cache installed, you can instruct django_rq to use the same connection information from your Redis cache. This has two advantages, it's DRY and it takes advantage of any optimization that may be going on in your cache setup (like using connection pooling or Hiredis.) -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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HyperTools
A Python toolbox for gaining geometric insights
...It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes, text or (mixed) lists. Applying topic models and other text vectorization methods to text data. HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. ... -
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DeepSpeed
Deep learning optimization library: makes distributed training easy
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can: 1. Train/Inference dense or sparse models with billions or trillions of parameters 2. Achieve excellent system throughput and efficiently scale to thousands of GPUs 3. Train/Inference on resource constrained GPU systems 4. Achieve unprecedented low latency and high throughput for inference 5. Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. ... -
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LEANN
Local RAG engine for private multimodal knowledge search on devices
LEANN is an open source system designed to enable retrieval-augmented generation (RAG) and semantic search across personal data while running entirely on local devices. It focuses on dramatically reducing the storage overhead typically required for vector search and embedding indexes, enabling efficient large-scale knowledge retrieval on consumer hardware. LEANN introduces a storage-efficient approximate nearest neighbor index combined with on-the-fly embedding recomputation to avoid storing... -
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...It uses large language models and multiple collaborating agents to simulate the typical cycle of research, experimentation, and improvement that human data scientists follow. It separates the process into two core phases: a research stage that proposes hypotheses and ideas, and a development stage that implements and evaluates them through code execution and experiments. By iterating through these stages, the framework continuously refines models and strategies using feedback from previous results. RD-Agent focuses heavily on automating complex tasks such as feature engineering, model design, and experimentation, which are traditionally time-consuming in machine learning and quantitative research workflows. ... -
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Sage Chat
Chat with any codebase in under two minutes | Fully local
Sage is an open-source AI developer assistant designed to help engineers understand and work with complex codebases more effectively. The tool functions similarly to an intelligent research agent that can analyze a repository and answer questions about how the software works. Instead of focusing solely on code generation, Sage emphasizes code comprehension, system architecture analysis, and integration guidance. Developers can ask natural language questions about a project, and the system... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. Users write Python-like code (via DataFrame APIs or SQL strings) to express... -
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gusty
Making DAG construction easier
...All you have to do is provide a list of dependencies or external_dependencies inside of a task file, and gusty will automatically set each task's dependencies and create external task sensors for any external dependencies listed. gusty works with both Airflow 1.x and Airflow 2.x, and has even more features, all of which aim to make the creation, management, and iteration of DAGs more fluid, so that you can intuitively design your DAG and build your tasks. -
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django-environ
Django-environ allows you to utilize 12factor inspired environment
...These strings from os.environ are loaded from a .env file and filled in os.environ with setdefault method, to avoid overwriting the real environment. A similar approach is used in Two Scoops of Django book and explained in the 12factor-Django article. django-environ is the Python package that allows you to use the Twelve-factor methodology to configure your Django application with environment variables.For that, it gives you an easy way to configure Django application using environment variables obtained from an environment file and provided by the OS. -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used... -
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Amazon Braket Python Schemas
A library that contains schemas for Amazon Braket
...The preferred way to get Amazon Braket Python Schemas is by installing the Amazon Braket Python SDK, which will pull in the schemas. You can install from source by cloning this repository and running a pip install command in the root directory of the repository. There are currently two types of IR, including jaqcd (JsonAwsQuantumCircuitDescription) and annealing. -
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DeepCTR
Package of deep-learning based CTR models
...Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique. -
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CycleGAN and pix2pix in PyTorch
Image-to-Image Translation in PyTorch
CycleGAN and pix2pix in PyTorch repository is a PyTorch implementation of two influential image-to-image translation frameworks: CycleGAN (for unpaired translation) and pix2pix (for paired translation). This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. -
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Google DeepMind GraphCast and GenCast
Global weather forecasting model using graph neural networks and JAX
GraphCast, developed by Google DeepMind, is a research-grade weather forecasting framework that employs graph neural networks (GNNs) to generate medium-range global weather predictions. The repository provides complete example code for running and training both GraphCast and GenCast, two models introduced in DeepMind’s research papers. GraphCast is designed to perform high-resolution atmospheric simulations using the ERA5 dataset from ECMWF, while GenCast extends the approach with diffusion-based ensemble forecasting for probabilistic weather prediction. Both models are built on JAX and integrate advanced neural architectures capable of learning from multi-scale geophysical data represented on icosahedral meshes. ... -
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Perception Models
State-of-the-art Image & Video CLIP, Multimodal Large Language Models
Perception Models is a state-of-the-art framework developed by Facebook Research for advanced image and video perception tasks. It introduces two primary components: the Perception Encoder (PE) for visual feature extraction and the Perception Language Model (PLM) for multimodal decoding and reasoning. The PE module is a family of vision encoders designed to excel in image and video understanding, surpassing models like SigLIP2, InternVideo2, and DINOv2 across multiple benchmarks. ... -
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OpenAI Swarm
Educational framework exploring multi-agent orchestration
Swarm focuses on making agent coordination and execution lightweight, highly controllable, and easily testable. It accomplishes this through two primitive abstractions; Agents and handoffs. An Agent encompasses instructions and tools, and can at any point choose to hand off a conversation to another Agent. These primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solutions while avoiding a steep learning curve. ... -
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Surya
Implementation of the Surya Foundation Model for Heliophysics
Surya is an open‑source, AI‑based foundation model for heliophysics developed collaboratively by NASA (via the IMPACT AI team) and IBM. Named after the Sanskrit word for “sun,” Surya is trained on nine years of high‑resolution solar imagery from NASA’s Solar Dynamics Observatory (SDO). It is designed to forecast solar phenomena—such as flares, solar wind, irradiance, and active region behavior—by predicting future solar images with a sophisticated long–short vision transformer architecture,...
